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VK-Finder Project

Chat Bot for VK Company to help you find your significant other.

The bot searches for people matching the conditions based on user information from VK:

  • age,
  • sex,
  • city,
  • family status.

For those people who meet the requirements, the user shows the top 3 popular profile photos in the chat along with a link to the found person. Popularity is determined by the number of likes and comments.

Built with:

Project setup steps

1. Project requirements

pip install -r requirements.txt

2. Create a database vk_finder or connect to an existing one.

createdb -U <OWNER_NAME> vk_finder

3. Create .env file with environment variables in folder path:

vk_finder/config/.env

You need to specify VK_USER_TOKEN, VK_GROUP_TOKEN and DSN.

User token can be obtained here

DSN is a data source name to your PostgreSQL database.

postgresql://demo:demo@localhost:port/db

4. Table migrations to the database.

Create an initial Alembic migration for an existing database.

Run commands in terminal

alembic init vk_finder/migrations

Configuration of the env.py file

Open the file in vk_finder/migrations/env.py and add the following changes to it.

We import the libraries to get the environment variables, run the necessary methods to get the variables and set them in the settings:

import os

from dotenv import find_dotenv, load_dotenv


load_dotenv(find_dotenv())

config = context.config
config.set_main_option('sqlalchemy.url', os.getenv('DSN'))

Adding a MetaData object to our model to support "autogeneration"

from vk_finder.database.models import Base


target_metadata = Base.metadata

To complete the migrations, run the following commands in the terminal

alembic revision --autogenerate -m "Init migration"
alembic upgrade head